The New Pro Se: Generative AI and the Surge in Federal Civil Self-Representation

Since public access to generative AI tools became widespread, federal civil litigation has seen a marked increase in pro se (self-represented) plaintiffs. This paper analyzes that shift using ~2.8 million filings, asking whether the post-GenAI period is associated not only with more pro se filings, but also with detectable changes in complaint text, litigation outcomes, and the composition of pro se litigants. Using civil filing data from FY2008–2025, we find that the federal civil pro se plaintiff rate rose from 11.33% pre-GenAI to 16.94% post-GenAI, a 5.61 percentage-point increase that persists after trend and covariate-adjusted robustness checks. We then focus on Civil Rights and Other Statutory cases, where the increase is especially pronounced, and link case metadata to pro se complaints. Drawing on stylometric AI detection indicators, we develop an interpretable measure of AI-consistent drafting. Against a threshold calibrated to the pre-GenAI baseline, the net AI-flagged share is 13.9% of post-GenAI non-form complaints. Analysis of the AI-flagged complaints shows that they are more citation-dense, disproportionately associated with observed first-time filers, gender, and geographically unevenly distributed. We find no evidence for improved win rates; in fact, AI-flagged complaints are more likely to be dismissed and terminate at earlier procedural phases. These findings raise new questions about access to justice and court screening burdens, and sharpen the distinction between legal formality and legal efficacy. GenAI may expand access to drafting without necessarily improving legal viability.